Biomedical Literature Mining for Biomedical Relation Extraction - Jahiruddin, (International Journal of Computer Sciences and Engineering, 2018)
Research work in the biomedical domain has been increasing at fast pace. Hence, the knowledge in the field of biomedical domain is growing exponentially. Consequently, the number of text documents containing the knowledge in this field is growing very rapidly. It is often very difficult for researchers to track the knowledge and assimilate it for generating new ideas. Therefore, it is highly desirable to organize such documents for extracting useful information from textual literature and store them in a structured form. As this information is embedded within text, so it is a challenging task to extract them. This paper presents a rule based system to extract biomedical relations along with biomedical entities from biomedical literatures. The system first generates a dependency tree of each sentence of a given literature, and then the rules are applied to extract the information components. The biomedical relations are embedded within these information components. Further, these information components are used to get feasible biomedical relations from a set of abstracts of biomedical literature. Furthermore, the system has been validated on a corpus of 500 abstracts downloaded from PubMed database on Alzheimer key word.